mlsanityData to 7 Oct 2026

Physics-Informed Functional Link Constrained Framework with Domain Mapping for Solving Bending Analysis of an Exponentially Loaded Perforated Beam

arXiv 2604.07025 ↗First posted 8 Apr 2026math.DS cs.LG cs.NAcited by 1

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Abstract (arXiv)

This article presents a novel and comprehensive approach for analyzing bending behavior of the tapered perforated beam under an exponential load. The governing differential equation includes important factors like filling ratio (α), number of rows of holes (N), tapering parameters (φ and psi), and exponential loading parameter (γ), providing a realistic and flexible representation of perforated beam configuration. Main goal of this work is to see how well the Domain mapped physics-informed Functional link Theory of Functional Connection (DFL-TFC) method analyses bending response of perforated beam with square holes under exponential loading. For comparison purposes, a corresponding PINN-based formulation is developed. Outcomes clearly show that the proposed DFL-TFC framework gives better results, including faster convergence, reduced computational cost, and improved solution accuracy when compared to the PINN approach. These findings highlight effectiveness and potential of DFL-TFC method for solving complex engineering problems governed by differential equations. Within this framework, hidden layer is replaced by a functional expansion block that enriches input representation via orthogonal polynomial basis functions, and the domain of DE mapped to corresponding domain of orthogonal polynomials. A Constrained Expression (CE), constructed through the Theory of Functional Connections (TFC) using boundary conditions, ensures that constraints are exactly satisfied. In CE, free function is represented using a Functional Link Neural Network (FLNN), which learns to solve resulting unconstrained optimization problem. The obtained results are further validated through the Galerkin and PINN solutions.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 31 references that are in the map. First posted on arXiv 8 Apr 2026.

Builds on

6 of 31 references.

References · 6

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020When and why PINNs fail to train: A neural tangent kernel perspectivecited by 629
  3. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 603
  4. 2018Deep Theory of Functional Connections: A New Method for Estimating the Solutions of PDEscited by 17
  5. 2017The Theory of Connections. Connecting Pointscited by 10
  6. 2023Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulationscited by 8

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Citing papers per year: 2026: 1.

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